Modeling protein loops with knowledge-based prediction of sequence-structure alignment

Bioinformatics. 2007 Nov 1;23(21):2836-42. doi: 10.1093/bioinformatics/btm456. Epub 2007 Sep 7.

Abstract

Motivation: As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches.

Results: We developed a knowledge-based loop prediction method that circumvents the need of constructing hierarchically clustered length-dependent loop libraries. The method first predicts local structural fragments of a query loop sequence and then structurally aligns the predicted structural fragments to a set of non-redundant loop structural templates regardless of the loop length. The sequence-template alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes. Prediction accuracy benchmarks indicated that the novel procedure provided an alternative approach overcoming the challenges of knowledge-based loop prediction.

Availability: http://cmb.genomics.sinica.edu.tw

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Artificial Intelligence*
  • Computer Simulation
  • Models, Chemical*
  • Models, Molecular*
  • Molecular Sequence Data
  • Pattern Recognition, Automated / methods
  • Protein Conformation
  • Proteins / chemistry*
  • Proteins / ultrastructure*
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Structure-Activity Relationship

Substances

  • Proteins